Generating featureless service provider matches

ABSTRACT

The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilize featureless supervised or unsupervised machine learning to generate service provider match predictions based on raw communication data. In one or more embodiments, the disclosed systems utilize a trained service provider match neural network to generate a service provider match prediction based on raw communication data associated with an incoming audio, text, or video communication. In response to the generated service provider match prediction, the disclosed systems can route the incoming communication to the matched service provider.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/549,186 filed on Aug. 23, 2019. The contents of which are herebyincorporated by reference.

BACKGROUND

Recent years have seen significant improvement in hardware and softwareplatforms for communications routing. Indeed, conventional systemsidentify attributes or profiles associated with an incomingcommunication and utilize those attributes or profiles to determine howthe communication should be routed. For example, in response toreceiving an incoming text message, conventional systems can analyze thetext message to identify a geographical origin of the text message, andcan then route the text message to a service provider (e.g., a customerservice agent) who is located within the same geographical area.

Although conventional systems can route communications, thisattribute-based approach gives rise to a number of technologicalproblems in accuracy, flexibility, and efficiency. For example, theattribute-based routing methodology utilized by conventional systemsrelies on a host of arbitrary assumptions and often leads to inaccurateservice provider matches. In such circumstances, conventional systemsfail to accurately route a communication to the most appropriate serviceprovider simply because attributes of the communication happened tocorrespond with a different service provider. This approach can lead torouting a communication to a wrong service provider, leading to multiplerouting attempts, which results in an inefficient use of communicationresources.

Moreover, conventional systems are rigid and inflexible. Indeed, as justmentioned, conventional systems are rigidly tied to predefinedconnections between attributes and profiles associated with an incomingcommunication and attributes and profiles associated with a particularservice provider. Because of this inflexibility, conventional systemsoften fail to route communications in such a way that will maximizetarget metrics that are customized to a particular organization or thatwill increase the accuracy and efficiency of routing a communication tothe most appropriate service provider available.

In addition to shortcomings with regard to accuracy and flexibility,conventional systems are also inefficient. As an initial matter,conventional systems expend significant resources in analyzingcommonalties in attributes and profiles between incoming communicationsand service providers to route the incoming communications. This leadsto inefficient utilization of computational resources by routing theincoming communications in a way that is not truly optimized to thespecific goals and metrics of an organization.

Thus, there are several technical problems with regard to conventionalcommunication routing systems.

SUMMARY

One or more embodiments provide benefits and/or solve one or more of theforegoing or other problems in the art with systems, methods, andnon-transitory computer readable storage media that utilize featurelesssupervised or unsupervised machine learning to accurately, flexibly, andefficiently route incoming communications to matched service providersso as to optimize one or more metrics customized to a particularorganization. In particular, an unsupervised matching system, asdescribed herein, generates matches between an incoming communicationand service providers without relying on any attribute or profileextracted from or associated with either the communication or theservice provider.

For example, in one or more embodiments, the disclosed systems train aservice provider match neural network with training inputs that includeraw communication training data that is untethered from any additionalinformation associated with the communications themselves. By applyingthe trained service provider match neural network to raw communicationdata associated with a new incoming communication, the unsupervisedmatching system can generate a service provider match prediction thatwill accurately, flexibly, and efficiently utilize computing resourcesin optimizing one or more target metrics associated with the incomingcommunication.

Additional features and advantages of one or more embodiments of thepresent disclosure will be set forth in the description that follows,and in part will be obvious from the description, or may be learned bypractice of such example embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is described with reference to the accompanyingdrawings in which:

FIG. 1 illustrates an example environment in which an unsupervisedmatching system operates in accordance with one or more embodiments;

FIG. 2 illustrates a flowchart of a series of acts in routing acommunication to a matched service provider in accordance with one ormore embodiments;

FIG. 3 illustrates a diagram of generating a service provider matchprediction in accordance with one or more embodiments;

FIG. 4A illustrates a flowchart of a series of acts in utilizing rawcommunication data to generate a service provider match prediction inaccordance with one or more embodiments;

FIG. 4B illustrates a flowchart of a series of acts in routing acommunication based on a generated service provider match prediction inaccordance with one or more embodiments;

FIG. 5 illustrates a diagram of training a service provider match neuralnetwork in accordance with one or more embodiments;

FIG. 6 illustrates a schematic diagram of the unsupervised matchingsystem in accordance with one or more embodiments;

FIG. 7 illustrates a flowchart of a series of acts for generating aservice provider match prediction in accordance with one or moreembodiments;

FIG. 8 illustrates an example computing device in accordance with one ormore embodiments; and

FIG. 9 illustrates an example communication system in accordance withone or more embodiments.

DETAILED DESCRIPTION

One or more embodiments of the present disclosure includes a featurelessmatching system that utilizes machine learning (e.g., supervisedlearning, unsupervised learning, trained neural networks) to accuratelygenerate service provider match predictions such that an incomingcommunication can be efficiently routed to the predicted serviceprovider. For example, the unsupervised matching system can train aservice provider match neural network with raw communication data togenerate service provider match predictions. Upon training, and whenprovided with a new incoming communication, the service provider matchneural network can generate a service provider match prediction that isbased on raw communication data, rather than attribute or profileinformation associated with the communication or the predicted serviceprovider. Thus, the unsupervised matching system avoids the pitfalls ofconventional systems by accurately, flexibly, and efficiently generatingservice provider matches and routing incoming communications withoutrelying on arbitrary and pre-defined rules and connections.

To illustrate, in one or more embodiments, the unsupervised matchingsystem relies on raw communication data. For example, conventionalsystems may extract attributes associated with an incoming communicationsuch as a location of origin (e.g., determined based on a phone numberarea code), a user profile (e.g., associated with a user ID), a time ofday (e.g., taked from an email header), and so forth. A conventionalsystem may then utilize these extracted attributes to route thecommunication to a service provider who satisfies a pre-defined ruleassociated with those extracted attributes.

Instead of this attribute-based approach, various embodiments of theunsupervised matching system matches and routes an incomingcommunication based on raw communication data associated with thatcommunication. For example, if the incoming communication is a telephonecall, the unsupervised matching system utilizes raw audio dataassociated with that telephone call to route the incoming communication.Similarly, if the incoming communication is a video communication or atext communication, the unsupervised matching system utilizes the rawvideo data or raw text data associated with the communication to routethe incoming communication.

As mentioned, in at least one embodiment, the unsupervised matchingsystem provides the raw communication data associated with an incomingcommunication to a trained service provider match neural network togenerate a service provider match prediction. In one or moreembodiments, the unsupervised matching system trains the serviceprovider match neural network to generate service provider matchpredictions utilizing raw communication training data and correspondingservice provider match markers. Specifically, the unsupervised matchingsystem applies the service provider match neural network to inputvectors associated with raw communication training data. Upon receivinga service provider match prediction from the service provider matchneural network, the unsupervised matching system compares the serviceprovider match prediction to the service provider match markerassociated with the raw communication training data. The unsupervisedmatching system can then modify parameters of the service provider matchneural network based on the comparison.

After training the service provider match neural network, theunsupervised matching system can apply the service provider match neuralnetwork to new incoming communications in order to generate serviceprovider match predictions. With each prediction, the unsupervisedmatching system can either route the communication to the predictedservice provider, or can provide the service provider match predictionto a third-party customer request system for further use. In at leastone embodiment, the unsupervised matching system may determine that apredicted service provider is not available to receive an incomingcommunication. In that event, the unsupervised matching system canmodify one or more parameters of the service provider match neuralnetwork in order to generate a second service provider match predictionso that the unsupervised matching system routes the communication to thebest available service provider based on the raw communication data.

The unsupervised matching system provides many advantages and benefitsover conventional systems and methods. For example, by utilizing rawcommunication data to generate service provider match predictions, theunsupervised matching system improves accuracy relative to conventionalsystems. Specifically, the unsupervised matching system can accuratelygenerate a service provider match prediction for an incomingcommunication, even when no additional attributes are known aboutincoming communication. Rather, the unsupervised matching systemgenerates service provider match predictions based wholly on the rawcommunication data associated with the communication.

Moreover, by avoiding the attribute-based approach of conventionalsystems, the unsupervised matching system improves flexibility relativeto conventional systems. For example, as discussed above, conventionalsystems are rigidly tied to attribute-based rules and pre-definedconnections in routing incoming communications. As such, conventionalsystems are unable to route incoming communications with no associatedattributes, or simply routes the communication based on no logic.Conversely, the unsupervised matching system flexibly matches and routesa communication based on that communication's raw communication data.Furthermore, by generating service provider match predictions based onraw communication data, the unsupervised matching system improvesefficiency relative to conventional systems. Specifically, theunsupervised matching system does not waste system resources in routingcommunications to service providers who are poor matches, which causesadditional transfers and/or additional communication based on the poormatch.

As illustrated by the foregoing discussion, the present disclosureutilizes a variety of terms to describe features and advantages of theunsupervised matching system. Additional detail is now providedregarding the meaning of such terms. For example, as used herein, theterm “communication” refers to any type of communication that theunsupervised matching system receives from a contacting user. Forexample, a communication can be a phone call (e.g., VoIP, cellular,POTS), a chat message, a text message (e.g., SMS), video call data(e.g., a video stream), an instant message (e.g., via social media), orany other type of electronic communication.

As mentioned above, the unsupervised matching system utilizes rawcommunication data associated with a communication. As used herein, theterm “raw communication data” refers to non-attribute based informationassociated with a communication. For example, raw communication dataextracted from a phone call communication can include a digitalrecording of at least a portion of the audio of the phone call.Similarly, raw communication data extracted from a text message caninclude digital information representing the textual contents of themessage. Similarly, raw communication data can include an analysis ofvideo data and/or image data.

Additionally, as used herein, the term “contacting user” refers to auser that contacts the unsupervised matching system. A contacting usercan refer to a user that is experiencing a problem or issue with aservice system and that contacts the unsupervised matching system in anattempt to resolve the problem or to inform others of the problem. Forexample, a contacting user can include a caller, a text chat user, avideo chat user, or a text message user that sends a communication tothe unsupervised matching system. As used herein, the term “serviceprovider” refers to a user who provides a specific type of service viaphone, text, and/or video. For example, a service provider may be acustomer service representative who provides technical support tocontacting users over the telephone. In another example, a serviceprovider may be a personal shopper who curates clothing choices for acontacting user over a video chat.

As mentioned above, the unsupervised matching system can utilize variousmachine learning techniques to generate service provider matchpredictions. For example, in one embodiment, the unsupervised matchingsystem may not be unsupervised. For instance, the matching system mayutilize supervised machine learning to generate provider matchpredictions from raw data. To illustrate, in supervised machinelearning, the matching system utilizes known inputs and known outputs(e.g., monitored raw data and match predictions collected over time) to“learn” a mapping function such that the matching system can predict newoutputs (e.g., new provider match predictions) when presented with newinputs (e.g., new raw data).

Alternatively, the matching system may be unsupervised. For example, thematching system may utilize known inputs with no corresponding outputsin order to model underlying functions such that when presented with newinputs (e.g., new raw data), the model can generate provider matchpredictions. In some embodiments, this type of unsupervised learning isalso called “deep learning” and may include one or more neural networks.Although as described herein, the unsupervised matching system utilizesone or more neural networks to generate provider match predictions, itwill be understood that in alternative embodiments, the matching systemcan utilize other types of machine learning to generate provider matchpredictions, as discussed above.

As used herein, the term “neural network” refers to a machine learningmodel that can be tuned (e.g., trained) based on inputs to approximateunknown functions. In particular, the term neural network can include amodel of interconnected artificial neurons (or layers) that communicateand learn to approximate complex functions and generate outputs based ona plurality of inputs provided to the model. In particular, a neuralnetwork includes a computer-implemented algorithm that implements deeplearning techniques to analyze input (e.g., training input encoded as aneural network input vector) to make predictions and that improves inaccuracy by comparing generated predictions against ground truth dataand modifying internal parameters for subsequent predictions. In someembodiments, a neural network can employ supervised learning, while inother embodiments a neural network can employ unsupervised learning orreinforced learning. Examples of neural networks include deepconvolutional neural networks, generative adversarial neural networks,and recurrent neural networks.

Relatedly, the term “train” refers to utilizing information to tune orteach a neural network. The term “training” (used as an adjective ordescriptor, such as “training data”) refers to information or datautilized to tune or teach the neural network. In some embodiments, theunsupervised matching system trains the service provider match neuralnetwork to generate accurate service provider match predictions based onrespective training data.

As mentioned above, the unsupervised matching system trains the serviceprovider match neural network with raw communication training data andcorresponding service provider match markers. As used herein, “rawcommunication training data” refers to raw communication data associatedwith a communication that has previously been matched with and/or routedto a service provider with optimized results. Also as used herein, a“service provider match marker” refers to a ground truth associated withthe corresponding raw communication data. To illustrate, a serviceprovider match marker can include a service provider ID and/orinformation associated with the service provider to whom thecommunication associated with the corresponding raw communicationtraining data was previously routed.

Additional detail regarding the communication management system will nowbe provided with reference to the figures. For example, FIG. 1illustrates a schematic diagram of an example environment forimplementing an unsupervised matching system 102 in accordance with oneor more embodiments. An overview of the unsupervised matching system 102is described in relation to FIG. 1. Thereafter, a more detaileddescription of the components and processes of the unsupervised matchingsystem 102 is provided in relation to the subsequent figures.

As shown in FIG. 1, the environment includes a server(s) 106, clientdevices 108 a-108 d, service provider devices 112 a-112 d, the thirdparty server(s) 120, and a network 122. Each of the components of theenvironment can communicate via the network 122, and the network 122 maybe any suitable network over which computing devices can communicate.Example networks are discussed in more detail below in relation to FIGS.8 and 9.

As mentioned, the environment includes different user devices such asthe client devices 108 a-108 d and the service provider devices 112a-112 d. The client devices 108 a-108 d can be a one of a variety ofcomputing devices, including a smartphone, tablet, smart television,desktop computer, laptop computer, virtual reality device, augmentedreality device, or other computing device as described in relation toFIGS. 8 and 9. Although FIG. 1 illustrates a four client devices 108 a,108 b, 108 c, and 108 d, in some embodiments the environment can includeadditional or fewer client devices, each associated with a differentcontacting user. The client devices 108 a-108 d can each receive userinput and other information and provide the information (including acommunication) to the server(s) 106. Thus, the unsupervised matchingsystem 102 on the server(s) 106 can receive communications to use ingenerating service provider match predictions and routing guidance.

As shown, each of the client devices 108 a-108 d include a communicationapplication (e.g., the communication applications 110 a, 110 b, 110 c,and 110 d, respectively). In particular, the communication applications110 a-110 d may be web applications, native applications installed onthe client devices 108 a-108 d (e.g., mobile applications, desktopapplications, etc.), or cloud-based applications where part of thefunctionality is performed by the server(s) 106 and/or the third partyserver(s) 120. The communication applications 110 a-110 d can present ordisplay information to a contacting user, including information relatingto options for creating and processing a communication to help acontacting user accomplish a desired purpose. A contacting user caninteract with one of the communication applications 110 a-110 d toprovide a communication that, for example, includes a customer servicerequest.

As also mentioned, the environment includes the service provider devices112 a, 112 b, 112 c, and 112 d. Like the client devices 108 a-108 d, theservice provider devices 112 a-112 d can each be one of a variety ofcomputing devices, including a smartphone, tablet, smart television,desktop computer, laptop computer, virtual reality device, augmentedreality device, or other computing device as described in relation toFIGS. 8 and 9. Although FIG. 1 illustrates four service provider devices112 a-112 d, in some embodiments the environment can include additionalor fewer service provider devices, each associated with a differentservice provider. The service provider devices 112 a-112 d can eachreceive user input from a service provider and can receive information(including a communication, contacting user information, a serviceprovider match prediction, and/or other information) from the server(s)106 and/or the third party server(s) 120.

As shown, the service provider devices 112 a-112 d include an agentapplication (e.g., the agent applications 114 a, 114 b, 114 c, and 114d, respectively). In particular, each of the agent applications 114a-114 d may be a web application, a native application installed on anagent device (e.g., a mobile application, a desktop application, etc.),or a cloud-based application where part of the functionality isperformed by the server(s) 106 and/or the third party server(s) 120. Theagent applications 114 a-114 d can present or display information to aservice provider, including a user interface for interacting with acontacting user and presenting information pertaining to the contactinguser (e.g., a routed communication, raw communication data, etc.). Aservice provider can interact with one of the agent applications 114a-114 d to, for example, help a contacting user reschedule a flight orreplace a defective product.

As illustrated in FIG. 1, the environment includes a third partyserver(s) 120 hosting a customer request system 116 and a customerdatabase 118. The customer request system 116 may be a service system ora different customer support system. For example, the customer requestsystem 116 may be a service system that a contacting user needs tocontact to resolve an issue stemming from a problem of a differentsystem—e.g., an airline system that a contacting user calls toreschedule a flight due to being stuck in traffic. In some embodiments,as indicated by the dashed box of FIG. 1, the service provider devices112 a-112 d are associated or affiliated with the customer requestsystem 116. In these embodiments, the unsupervised matching system 102may provide information such as a communication and a generated serviceprovider match prediction to the customer request system 116, whereuponthe customer request system 116 may assign or relay the communication toa service provider device (e.g., one of the service provider devices 112a-112 d). In other embodiments, the service provider devices 112 a-112 dare not directly associated with the customer request system 116, andthe unsupervised matching system 102 provides communications directly tothe service provider device corresponding to service provider matchpredictions without necessarily utilizing the customer request system116.

As further illustrated in FIG. 1, the environment includes the server(s)106 hosting the communication management system 104, of which theunsupervised matching system 102 is a component. The communicationmanagement system 104 may generate, store, process, receive, andtransmit electronic data, such as communications, raw communicationdata, and service provider match predictions. For example, thecommunication management system 104 may receive data from the clientdevice 108 a in the form of a communication. In addition, thecommunication management system 104 can transmit data to one or more ofthe service provider devices 112 a-112 d and/or the servicer(s) 120 toprovide the communication and a generated service provider matchprediction. The communication management system 104 can communicate withthe client devices 108 a-108 d, the service provider devices 112 a-112d, and the third party server(s) 120 via the network 122. In someembodiments, the server(s) 106 hosting the communication managementsystem 104 comprises an application server, a communication server, aweb-hosting server, a social networking server, a digital contentcampaign server, or a digital content management server.

Although FIG. 1 depicts the unsupervised matching system 102 within thecommunication management system 104 and located on the server(s) 106, insome embodiments, the unsupervised matching system 102 may beimplemented by (e.g., located entirely or in part) on one or more othercomponents of the environment. For example, the unsupervised matchingsystem 102 may be implemented by any of the client devices 108 a-108 d,the service provider devices 112 a-112 d, and/or the third partyserver(s) 120.

In some embodiments, though not illustrated in FIG. 1, the environmentmay have a different arrangement of components and/or may have adifferent number or set of components altogether. For example, theclient devices 108 a-108 d, the service provider devices 112 a-112 d,and the third party server(s) 120 may communicate directly with theunsupervised matching system 102, bypassing the network 122.Additionally, the unsupervised matching system 102 can include one ormore databases (e.g., a database to store contacting user information,contextual information, and/or generated purpose predictions) housed onthe server(s) 106 or elsewhere in the environment. The unsupervisedmatching system 102 can be implemented in a variety of different waysacross the server(s) 106, the network 122, the service provider devices112 a-112 d, the client devices 108 a-108 d, and the third partyserver(s) 120. Additional detail regarding implementing differentcomponents of the unsupervised matching system 102 across devices isprovided below.

As discussed above, the unsupervised matching system 102 can generateservice provider match predictions based on raw communication data. Forinstance, FIG. 2 illustrates an overview of the unsupervised matchingsystem 102 generating service provider match predictions. Specifically,FIG. 2 illustrates the unsupervised matching system 102 performing anact 202 of receiving a communication from a client device. For example,in one or more embodiments, the unsupervised matching system 102receives a communication directly from a client device (e.g., one of theclient devices 108 a-108 d shown in FIG. 1). Alternatively oradditionally, the unsupervised matching system 102 can receivecommunications from client devices via the customer request system 116.As mentioned above, in one or more embodiments, a communication can beauditory, textual, or video-based.

As shown in FIG. 2, after receiving the communication, the unsupervisedmatching system 102 performs an act 204 of matching the communication toa service provider. For example, as mentioned above and in at least oneembodiment, the unsupervised matching system 102 matches thecommunication to a service provider by extracting raw communication dataassociated with the communication and providing the extracted rawcommunication data to a trained service provider match neural network.In response to receiving the raw communication data, the serviceprovider match neural network can generate a service provider matchprediction that indicates an optimal service provider who should berouted the communication.

Based on the output of the service provider match neural network, theunsupervised matching system 102 performs the act 206 of routing thecommunication to the matched service provider. For example, in oneembodiment, the unsupervised matching system 102 routes a communicationdirectly to the matched service provider. In that case, the unsupervisedmatching system 102 can provide the communication to the matched serviceprovider via the agent application (e.g., one of the agent applications114 a-114 d shown in FIG. 1) installed on the service provider's serviceprovider device. Additionally or alternatively, the unsupervisedmatching system 102 can route the communication to the matched serviceprovider via the customer request system 116. In that case, theunsupervised matching system 102 can provide the communication alongwith the service provider match prediction to the customer requestsystem 116, or can provide only the service provider match prediction ifthe customer request system 116 already has the communication (i.e., thecustomer request system 116 originally forwarded the communication tothe unsupervised matching system 102).

FIG. 3 illustrates additional detail with regard to generating servicematch predictions in accordance with one or more embodiments. Forexample, FIG. 3 shows the unsupervised matching system 102 performs anact 302 of receiving an auditory communication (e.g., a live or recordedphone call with a person speaking the words “I'm having a problemloading your software”). As mentioned above, in additional oralternative embodiments, the unsupervised matching system 102 canreceive textual communications (e.g., as an SMS text message, emailmessage, or other electronic message) and/or visual communications(e.g., as streaming or recorded video).

In response to receiving the communication 302, the unsupervisedmatching system 102 performs an act 304 of extracting raw communicationdata. In one or more embodiments, the unsupervised matching system 102extracts the raw communication data directly from the receivedcommunication. For example, the unsupervised matching system 102 cansample a portion of a live analog audio stream and can convert thesample to a digital file. More specifically, the unsupervised matchingsystem 102 can convert the same to a digital file by mapping the heightof the analog sound waves to a set of positive and negative integersthat replicate the wave in digital form.

Additionally or alternatively, rather than extracting the rawcommunication data directly from the received communication, theunsupervised matching system 102 can utilize the communication from aclient device to identify stored raw communication data associated withthe client device. For example, in at least one embodiment, thecommunication management system 104 and/or the customer request system116 stores raw communication data associated with client devices in acentral repository (e.g., the customer database 118). In thatembodiment, upon receiving the communication from the client device, theunsupervised matching system 102 can determine a user identifier (e.g.,a phone number, a profile name, an account number) associated with theclient device. The unsupervised matching system 102 can then utilize theuser identifier to extract the corresponding raw communication data fromthe repository.

With the raw communication data associated with the communicationextracted, the unsupervised matching system 102 provides the rawcommunication data to the service provider match neural network 306. Asshown in FIG. 3, and as an initial act 308, the service provider matchneural network 306 performs the act 308 of generating an input vectorbased on the raw communication data. In one or more embodiments, theservice provider match neural network 306 generates an input vector byencoding information from the raw communication data into a fixed-lengthvector representation. For example, the service provider match neuralnetwork 306 can generate the input vector by randomly sampling a portionof the raw communication data and embedding the sampled portion into afixed-length input vector.

After generating the input vector, the service provider match neuralnetwork 306 performs the act 310 of processing the input vector. Forexample, the service provider match neural network 306 can process theinput vector by providing the input vector to an input neural networklayer, and causing the subsequent layer output to be provided tosubsequent processing layers within the service provider match neuralnetwork 306 (e.g., LSTM layers, BiMax pooling layers, etc.).

Next, the service provider match neural network 306 performs the act 312of generating a service provider match prediction. As mentioned above,the one or more processing layers of the service provider match neuralnetwork 306 ultimately output a service provider match prediction. Inone or more embodiments, service provider match neural network 306generates a service provider match prediction including a serviceprovider identifier (e.g., an employee number, a service provider deviceID). Additionally or alternatively, the service provider match neuralnetwork 306 can generate a service provider match prediction including aservice provider's name, or characteristics of the service provider, aswill be explained in detail below.

With the service provider match prediction, the unsupervised matchingsystem 102 can perform the act 314 of routing the communication. Asmentioned above, the unsupervised matching system 102 can route thecommunication directly to the predicted service provider. Additionallyor alternatively, the unsupervised matching system 102 can route thecommunication by providing the service provider match prediction to thecustomer request system 116 hosted on the third-party server 120.

FIG. 4A illustrates additional detail with regard to generating serviceprovider match predictions. For example, FIG. 4A shows the unsupervisedmatching system 102 performing the act 402 of receiving a communicationfrom a client device. As discussed above, the communication may be aphone call, a text message, a social media communication, a videostream, a video recording, and so forth.

As illustrated in FIG. 4A and in response to receiving the communicationfrom the client device, the unsupervised matching system 102 nextperforms the act 404 of determining a user ID associated with thecommunication. For example, in at least one embodiment, rather thanexpending additional time and computing resources in extracting rawcommunication data from an incoming communication, the unsupervisedmatching system 102 stores raw communication data associated with usersover time. As discussed above, the unsupervised matching system 102 canstore this accumulated raw communication data in a central repository(e.g., the customer database 118). Additionally or alternatively, thecustomer request system 116 may collect and store raw communication dataover time in the customer database 118.

In one or more embodiments, the unsupervised matching system 102 maystore the collected raw communication data in various ways. For example,the unsupervised matching system 102 may store raw communication databased on user IDs. For instance, a client's user ID may be a telephonenumber (e.g., a telephone number associated with the client's clientdevice if the client device is capable of making phone calls).Additionally or alternatively, a client's user ID may be an accountnumber, a profile name, an email address, a screen name, a date ofbirth, or any other unique identifier.

In at least one embodiment, the unsupervised matching system 102determines a user ID associated with a communication by analyzing dataincluded in and/or associated with the communication. For example, ifthe communication is an email, the unsupervised matching system 102 maydetermine the user ID associated with the email by analyzing headerinformation included with the email to extract an email addressassociated with the sender. In another example, if the communication isan incoming video stream, the unsupervised matching system 102 maydetermine the user ID associated with the video stream by analyzing datapacket information associated with the video stream to extract an IPaddress, an account number, or a screen name. Moreover, in someembodiments, if the communication includes visual data (e.g., such aswith a digital image or video), the unsupervised matching system 102 maydetermine the user ID associated with the communication by performingimage analysis techniques (e.g., facial recognition) in connection withthe communication.

Once the unsupervised matching system 102 has determined a user IDassociated with the received communication, the unsupervised matchingsystem 102 can perform the act 406 of querying raw communicationassociated with the user ID from the repository of raw communicationdata. For example, as shown in FIG. 4A, the unsupervised matching system102 can generate and submit a database query based on the user ID to thecustomer database 118.

In one or more embodiments, the unsupervised matching system 102 queriesa predetermined unit of raw communication data from the customerdatabase 118 that is associated with the user ID. For example, if thereceived communication is a phone call, the unsupervised matching system102 may query ten seconds of raw audio data associated with the callingclient from the customer database 118. In another example, if thereceived communication is an SMS text message, the unsupervised matchingsystem 102 may query 50 characters of raw textual data associated withthe calling client from the customer database 118.

After querying the raw communication data from the data repository, theunsupervised matching system 102 performs the act 410 of generating aninput vector. As discussed above, the unsupervised matching system 102generates an input vector for the service provider match neural network306 that is configured or packaged in a manner that is customized to theinput layer of the service provider match neural network 306. It shouldbe noted that, at this point, the unsupervised matching system 102 nolonger relies on the determined user ID in generating the serviceprovider match prediction. Rather, as discussed above, the serviceprovider match neural network 306 generates the service provider matchprediction based on the raw communication data included in the inputvector. As such, the unsupervised matching system 102 next performs theacts 412 and 414 of applying the service provider match neural network306 to the generated input vector and generating the service providermatch prediction, as discussed above.

FIG. 4B illustrates additional features of the unsupervised matchingsystem 102. In one or more embodiments, the service provider indicatedby a service provider match prediction may not be available to receive acommunication. For example, if the matched service provider is acustomer service agent who responds to phone calls, the service providermay still be on a previously received phone call when the unsupervisedmatching system 102 matches that service provider to a newly receivedcommunication. Likewise, the matched service provider may be unavailabledue to being on a break or out sick. In those instances, theunsupervised matching system 102 can generate alternative serviceprovider match predictions.

For example, as illustrated in FIG. 4B, the unsupervised matching system102 can perform the acts 416, 418, and 420 of receiving a communication,determining a user ID associated with the communication, and generatingan input vector, as described above with reference to FIG. 4A. At act422, the unsupervised matching system 102 applies the service providermatch neural network 306 to the generated input vector in order togenerate a service provider match prediction. As discussed above, thegenerated service provider match prediction can include a serviceprovider ID, a service provider device ID (e.g., MAC address, IPaddress), a service provider name, or any other identifying informationassociated with the matched service provider.

At this point, the unsupervised matching system 102 performs the act 424of determining whether the matched service provider is available toreceive the communication. In one or more embodiments, the unsupervisedmatching system 102 makes this determination by providing the generatedservice provider match predication to the customer request system 116and then receiving an indication from the customer request system 116that the matched service provider is unavailable. Additionally oralternatively, the unsupervised matching system 102 can make thisdetermination by contacting the matched service provider directly andreceiving an indication from the service provider that he or she isunavailable. Additionally or alternatively, the unsupervised matchingsystem 102 can make this determination by contacting the matched serviceprovider directly and receiving no response from the matched serviceprovider after a threshold amount of time. Additionally oralternatively, the unsupervised matching system 102 can make thisdetermination by accessing information (e.g., timekeeping information,human resources information, etc.) that indicates whether a matchedservice provider is out sick, on vacation, or on a scheduled break.

If the unsupervised matching system 102 determines that the matchedservice provider is available (e.g., “Yes”), the unsupervised matchingsystem 102 performs the act 426 of sending the service provider ID(e.g., indicated by the generated service provider match prediction) tothe customer request system 116. As discussed above, in the event thatthe unsupervised matching system 102 does not contact the serviceproviders directly, the unsupervised matching system 102 can providematch and routing information to the customer request system 116 hostedby the third-party server(s) 120.

If the unsupervised matching system 102 determines that the matchedservice provider is not available (e.g., “No”), the unsupervisedmatching system 102 again performs the act 422 of applying the serviceprovider match neural network to the generated input vector. Forexample, in at least one embodiment, the service provider match neuralnetwork 306 includes latent memory features that “remember” matchedservice providers for a threshold amount of time. As such, when theunsupervised matching system 102 applies the service provider matchneural network 306 to the same input vector within a predeterminedperiod of time (e.g., 5 seconds), the service provider match neuralnetwork can remove the previously matched service provider fromconsideration. In that scenario, the service provider match neuralnetwork 306 can generate a service provider match prediction thatfeatures a different service provider. In this way, the service providermatch neural network 306 avoids unnecessary lag.

Additionally or alternatively, in at least one embodiment, theunsupervised matching system 102 can provide service provider availableinformation as part of the input vector. For example, the unsupervisedmatching system 102 can generate the input vector (e.g., in the act 420)to include raw communication data associated with the receivedcommunication as well as human resources information indicating theservice providers currently available. In at least one embodiment, theunsupervised matching system 102 can update the input vector each timethe service provider match neural network 306 is applied to include themost up-to-date availability information. In one or more embodiments,the unsupervised matching system 102 can continue to apply the serviceprovider match neural network to the input vector until the unsupervisedmatching system 102 determines that the matched service provider isavailable in act 424.

As discussed above, the unsupervised matching system 102 can train aservice provider match neural network 306 to generate service providermatch predictions. For instance, FIG. 5 illustrates training a serviceprovider match neural network 306 in accordance with one or moreembodiments. Specifically, as shown in FIG. 5, the unsupervised matchingsystem 102 identifies the match prediction training data 502. Forexample, as mentioned above, the unsupervised matching system 102 trainsthe service provider match neural network 306 based on raw communicationmatch data 504 and service provider match markers 506. In one or moreembodiments, the unsupervised matching system 102 trains the serviceprovider match neural network 306 by providing an instance of rawcommunication match data to the service provider match neural network306 as an input vector, comparing the output of the service providermatch neural network 306 to the service provider match marker thatcorresponds to the instance of raw communication match data, and thenmodifying the parameters of the service provider match neural network306 based on the comparison. This process will now be described ingreater detail.

As shown in FIG. 5, the match prediction training data 502 includes rawcommunication match data 504 and service provider match markers 506. Inone or more embodiments, the raw communication match data 504 includesinstances of raw communication data associated with previously receivedcommunications routed to service providers. For example, an instance ofraw communication match data 504 may include a sample (e.g., 5 seconds)of recorded phone call audio, a portion (e.g., 50 characters) of atextual communication such as an SMS text message or email, or a sample(e.g., 25 frames) of a video recording.

Each instance of raw communication match data 504 corresponds to aservice provider match marker 506. For example, every sample of recordedaudio corresponds to a service provider match marker 506 that includes aground truth (e.g., a service provider ID, or attributes of thepredicted service provider) associated with that sample of recordedaudio. Similarly, every portion of textual data and/or video data in theraw communication match data 504 corresponds to a service provider matchmarker 506 including the ground truth associated with that sample.

To begin training the service provider match neural network 306, theunsupervised matching system 102 provides an instance of rawcommunication match data 504 to the service provider match neuralnetwork 306. As discussed above, the service provider match neuralnetwork 306 may include the functionality to generate an input vectorfrom received raw communication data. Accordingly, in response toreceiving the instance of raw communication match data 504, the serviceprovider match neural network 306 may generate an input vector, and passthe generated input vector to an input layer of the service providermatch neural network 306.

Depending on the structure of the service provider match neural network306, the service provider match neural network 306 may process thegenerated input vector in a number of ways. For example, in oneembodiment, the service provider match neural network 306 may passlatent feature vectors between sequential processing layers of theservice provider match neural network 306. Additionally, the serviceprovider match neural network 306 may pool prediction results (e.g., ina max pooling layer), and generate a service provider match prediction.

After the service provider match neural network 306 generates a serviceprovider match prediction, the unsupervised matching system 102continues to train the service provider match neural network 306 byperforming the act 508 of comparing the service provider matchprediction to a corresponding service provider match marker. Forexample, as discussed above, for each instance of raw communicationmatch data 504, the match prediction training data 502 includes acorresponding service provider match marker 506. Each service providermatch marker 506 includes a ground truth indicating a service providerto whom a corresponding communication was successfully routed.

As mentioned above, a communication is “successfully” routed when apositive metric results from the communication being routed to aparticular service provider. For example, depending on the type ofservice provided by a service provider, a positive metric may be asuccessfully resolved customer service issue, a successful sale, apositive product review, a first-time service scheduling, are-scheduling of a service, a re-booking of a ticket, and so forth.

As such, the act 508 can involve the unsupervised matching system 102determining whether the service provider match prediction generated bythe service provider match neural network 306 is the same as the serviceprovider match marker 506 corresponding to the raw communication matchdata 504 provided to the service provider match neural network 306. Theunsupervised matching system 102 can determine that the generatedprediction is the same as the corresponding marker by utilizing a directcharacter comparison (e.g., via OCR). In additional embodiments, theunsupervised matching system 102 can determine that the generatedprediction is the same as the corresponding marker if the generatedprediction is “close enough.” For example, if the generated predictionand the corresponding marker both include service provider attributes(e.g., nationality, age, sex) rather than a specific service providerID, the unsupervised matching system 102 can determine that theprediction and the marker are the same if a threshold number ofattributes match.

To continue training the service provider match neural network 306, theunsupervised matching system 102 performs the act 510 of modifyingparameters of the service provider match neural network 306 based on thecomparison performed in the act 508. For example, based on thiscomparison, the unsupervised matching system 102 can modify parametersof one or more layers of the service provider match neural network 306to reduce the measure of loss.

In one or more embodiments, the unsupervised matching system 102continues training the service provider match neural network 306 untileither the match prediction training data 502 is exhausted or themeasure of loss is minimized and stable over a threshold number oftraining cycles. The unsupervised matching system 102 may periodicallyretrain the service provider match neural network 306 in the same mannerillustrated in FIG. 5. Once training of the service provider matchneural network 306 is complete, the unsupervised matching system 102 canapply the service provider match neural network 306 to unknown rawcommunication inputs (e.g., raw communication inputs with nocorresponding training markers).

As described in relation to FIGS. 1-5, the unsupervised matching system102 performs operations for training and utilizing a service providermatch neural network 306 to generate service provider match predictionsbased on raw communication data. FIG. 6 illustrates a detailed schematicdiagram of an embodiment of the unsupervised matching system 102described above. Although illustrated on the servicer(s) 106, asmentioned above, the unsupervised matching system 102 can be implementedby one or more different or additional computing devices (e.g., theservice provider device 112 a). In one or more embodiments, theunsupervised matching system 102 includes a communication manager 602,an input vector generator 604, a training manager 606, and a serviceprovider match neural network manager 608.

Each of the components 602-608 of the unsupervised management system 102can include software, hardware, or both. For example, the components602-608 can include one or more instructions stored on acomputer-readable storage medium and executable by processors of one ormore computing devices, such as a client device or server device. Whenexecuted by the one or more processors, the computer-executableinstructions of the unsupervised management system 102 can cause thecomputing device(s) to perform the methods described herein.Alternatively, the components 602-608 can include hardware, such as aspecial-purpose processing device to perform a certain function or groupof functions. Alternatively, the components 602-608 of the unsupervisedmanagement system 102 can include a combination of computer-executableinstructions and hardware.

Furthermore, the components 602-608 of the unsupervised managementsystem 102 may, for example, be implemented as one or more operatingsystems, as one or more stand-alone applications, as one or more modulesof an application, as one or more plug-ins, as one or more libraryfunctions or functions that may be called by other applications, and/oras a cloud-computing model. Thus, the components 602-608 may beimplemented as a stand-alone application, such as a desktop or mobileapplication. Furthermore, the components 602-608 may be implemented asone or more web-based applications hosted on a remote server. Thecomponents 602-608 may also be implemented in a suite of mobile deviceapplications or “apps.”

As mentioned above, and as shown in FIG. 6, the unsupervised matchingsystem 102 includes a communication manager 602. In one or moreembodiments, the communication manager 602 handles communicationsbetween the unsupervised matching system 102 and other computingdevices. For example, the communication manager 602 can send and receiveinformation (e.g., communications) to and from the client device 108 a.To illustrate, the communication manager 602 can receive an SMS textmessage communication from the client device 108 a via the communicationapplication 110 a. Additionally or alternatively, the communicationmanager 602 can send and receive information (e.g., communications,match predictions) to and from other devices and systems such as thecustomer request system 116 and the service provider device 112 a.

As further shown in FIG. 6, the unsupervised matching system 102includes an input vector generator 604. In one or more embodiments, theinput vector generator 604 generates an input vector based on acommunication received by the communication manager 602. For example, inone embodiment, the input vector generator 604 extracts rawcommunication data (e.g., a sample of raw audio data, a threshold numberof text characters, a threshold number of digital video frames) directlyfrom the received communication. In an additional or alternativeembodiment, the input vector generator 604 extracts a user ID associatedwith a received communication and utilizes the user ID to query rawcommunication data associated with the user ID from a central repositoryof raw communication data. With the raw communication data extractedand/or queried, the input vector generator 604 generates an input vectorspecific to the service provider match neural network 306 by configuringthe raw communication data into a standardized format.

As mentioned above, and as shown in FIG. 6, the unsupervised matchingsystem 102 includes a training manager 606. In one or more embodiments,the training manager 606 utilizes raw communication match data (e.g.,the raw communication match data 504 described with reference to FIG. 5)and service provider match markers (e.g., the service provider matchmarkers 506 described with reference to FIG. 5) to train a serviceprovider match neural network (e.g., the service provider match neuralnetwork 306). As described above, the training manager 606 trains aservice provider match neural network by providing raw communicationmatch data as input vectors to the service provider match neural networkand then comparing the output of the service provider match neuralnetwork to the associated service provider match markers. Based on thiscomparison, the training manager 606 then modifies parameters of theservice provider match neural network to reduce a measure of loss.

Furthermore, as mentioned above and as shown in FIG. 6, the unsupervisedmatching system 102 includes a service provider match neural networkmanager 608. In one or more embodiments, the service provider matchneural network manager 608 maintains the service provider match neuralnetwork, and signals a need for periodic re-training to the trainingmanager 606. For example, the service provider match neural networkmanager 608 can determine that the service provider match neural networkrequires re-training after a threshold amount of time, or after athreshold number of applications.

Although the unsupervised matching system 102 is described herein asgenerating service provider match predictions utilizing a serviceprovider match neural network, in additional embodiments, theunsupervised matching system 102 can generate service provider matchpredictions in other ways. For example, in one alternative embodiment,the unsupervised matching system 102 can generate service provider matchpredictions using data segmentation and topic modeling. To illustrate,based on a defined optimization goal (e.g., sales), the unsupervisedmatching system 102 can develop clusters of raw communication data andcorresponding service provider match predictions. The unsupervisedmatching system 102 can then identify one or more characteristics ofeach data cluster. Then when a new communication is received, theunsupervised matching system 102 can determine the best data clusterwith which to associated the new communication based on one or morecharacteristics of the raw communication data associated with the newcommunication, and can generate a service provider match predictionbased on the service provider match predictions within the identifiedcluster.

FIGS. 1-6, the corresponding text, and the examples provide a number ofdifferent methods, systems, devices, and non-transitorycomputer-readable media of the unsupervised matching system 102. Inaddition to the foregoing, one or more embodiments can also be describedin terms of flowcharts comprising acts for accomplishing a particularresult, as shown in FIG. 7. FIG. 7 may be performed with more or feweracts. Further, the acts may be performed in differing orders.Additionally, the acts described herein may be repeated or performed inparallel with one another or parallel with different instances of thesame or similar acts.

As mentioned, FIG. 7 illustrates a flowchart of a series of acts 700 forgenerating a service provider match prediction in accordance with one ormore embodiments utilizing an unsupervised matching system (e.g., aservice provider match neural network). While FIG. 7 illustrates actsaccording to one embodiment, alternative embodiments may omit, add to,reorder, and/or modify any of the acts shown in FIG. 7. The acts of FIG.7 can be performed as part of a method. Alternatively, a non-transitorycomputer-readable medium can comprise instructions that, when executedby one or more processors, cause a computing device to perform the actsof FIG. 7. In some embodiments, a system can perform the acts of FIG.10.

As shown in FIG. 7, the series of acts 700 includes an act 710 ofreceiving a communication. For example, the act 710 can involvereceiving a communication from a client device associated with acontacting user. For instance, receiving a communication from the clientdevice can include receiving one or more of an audio communication, avideo communication, or a text communication. In one or moreembodiments, the services of acts 700 can further include, in responseto receiving the communication from the client device, identifying auser ID associated with the contacting user.

Also as shown in FIG. 7, the series of acts 700 includes an act 720 ofgenerating an input vector based on raw communication data. For example,the act 720 can involve generating an input vector based on rawcommunication data associated with the contacting user. In one or moreembodiments, generating the input vector includes extracting the rawcommunication data from the received communication. For instance,generating the input vector further can include configuring theextracted raw communication data to match a standard input vector formatassociated with the unsupervised matching system. In at least oneembodiment, generating the input vector includes: identifying, from araw communication data repository and based on the user ID, the rawcommunication data associated with the contacting user, and configuringthe identified raw communication data to match a standard input vectorformat associated with the unsupervised matching system.

The series of acts 700 also includes an act 730 of generating a serviceprovider match prediction by providing the input vector to anunsupervised matching system. For example, the act 730 can involvegenerating a service provider match prediction by: providing the inputvector to an unsupervised matching system, wherein the unsupervisedmatching system is trained to generate a service provider match from rawcommunication training data.

Additionally, the series of acts 700 includes an act 740 of providingthe service provider match prediction to a customer request system. Forexample, the act 740 can involve, in response to generating the serviceprovider match prediction, providing the service provider matchprediction to a customer request system.

In one or more embodiments, the series of acts 700 further includes,prior to receiving the communication from the client device associatedwith the contacting user, training the unsupervised matching system togenerate the service provider match. For example, training theunsupervised matching system can include: generating a plurality oftraining input vectors based on raw communication training data, whereineach training input vector is associated with a corresponding serviceprovider match marker, and for each of the plurality of training inputvectors: providing the training input vector to the unsupervisedmatching system to generate a service provider match prediction, andcomparing the service provider match prediction with the correspondingservice provider match marker to modify parameters of the unsupervisedmatching system.

In one or more embodiments, the series of acts 700 further includes:receiving, from the customer request system, an indication that theservice provider match prediction cannot be accommodated, in response toreceiving the indication, modifying one or more parameters of theunsupervised matching system, and generating a second service providermatch prediction utilizing the unsupervised matching system.

Embodiments of the present disclosure may comprise or utilize a specialpurpose or general-purpose computer including computer hardware, suchas, for example, one or more processors and system memory, as discussedin greater detail below. Embodiments within the scope of the presentdisclosure also include physical and other computer-readable media forcarrying or storing computer-executable instructions and/or datastructures. In particular, one or more of the processes described hereinmay be implemented at least in part as instructions embodied in anon-transitory computer-readable medium and executable by one or morecomputing devices (e.g., any of the media content access devicesdescribed herein). In general, a processor (e.g., a microprocessor)receives instructions, from a non-transitory computer-readable medium,(e.g., a memory, etc.), and executes those instructions, therebyperforming one or more processes, including one or more of the processesdescribed herein.

Computer-readable media can be any available media that can be accessedby a general purpose or special purpose computer system.Computer-readable media that store computer-executable instructions arenon-transitory computer-readable storage media (devices).Computer-readable media that carry computer-executable instructions aretransmission media. Thus, by way of example, and not limitation,embodiments of the disclosure can comprise at least two distinctlydifferent kinds of computer-readable media: non-transitorycomputer-readable storage media (devices) and transmission media.

Non-transitory computer-readable storage media (devices) includes RAM,ROM, EEPROM, CD-ROM, solid state drives (“SSDs”) (e.g., based on RAM),Flash memory, phase-change memory (“PCM”), other types of memory, otheroptical disk storage, magnetic disk storage or other magnetic storagedevices, or any other medium which can be used to store desired programcode means in the form of computer-executable instructions or datastructures and which can be accessed by a general purpose or specialpurpose computer.

A “network” is defined as one or more data links that enable thetransport of electronic data between computer systems and/or modulesand/or other electronic devices. When information is transferred orprovided over a network or another communications connection (eitherhardwired, wireless, or a combination of hardwired or wireless) to acomputer, the computer properly views the connection as a transmissionmedium. Transmissions media can include a network and/or data linkswhich can be used to carry desired program code means in the form ofcomputer-executable instructions or data structures and which can beaccessed by a general purpose or special purpose computer. Combinationsof the above should also be included within the scope ofcomputer-readable media.

Further, upon reaching various computer system components, program codemeans in the form of computer-executable instructions or data structurescan be transferred automatically from transmission media tonon-transitory computer-readable storage media (devices) (or viceversa). For example, computer-executable instructions or data structuresreceived over a network or data link can be buffered in RAM within anetwork interface module (e.g., a “NIC”), and then eventuallytransferred to computer system RAM and/or to less volatile computerstorage media (devices) at a computer system. Thus, it should beunderstood that non-transitory computer-readable storage media (devices)can be included in computer system components that also (or evenprimarily) utilize transmission media.

Computer-executable instructions comprise, for example, instructions anddata which, when executed at a processor, cause a general-purposecomputer, special purpose computer, or special purpose processing deviceto perform a certain function or group of functions. In someembodiments, computer-executable instructions are executed on ageneral-purpose computer to turn the general-purpose computer into aspecial purpose computer implementing elements of the disclosure. Thecomputer executable instructions may be, for example, binaries,intermediate format instructions such as assembly language, or evensource code. Although the subject matter has been described in languagespecific to structural features and/or methodological acts, it is to beunderstood that the subject matter defined in the appended claims is notnecessarily limited to the described features or acts described above.Rather, the described features and acts are disclosed as example formsof implementing the claims.

Those skilled in the art will appreciate that the disclosure may bepracticed in network computing environments with many types of computersystem configurations, including, personal computers, desktop computers,laptop computers, message processors, hand-held devices, multi-processorsystems, microprocessor-based or programmable consumer electronics,network PCs, minicomputers, mainframe computers, mobile telephones,PDAs, tablets, pagers, routers, switches, and the like. The disclosuremay also be practiced in distributed system environments where local andremote computer systems, which are linked (either by hardwired datalinks, wireless data links, or by a combination of hardwired andwireless data links) through a network, both perform tasks. In adistributed system environment, program modules may be located in bothlocal and remote memory storage devices.

Embodiments of the present disclosure can also be implemented in cloudcomputing environments. In this description, “cloud computing” isdefined as a model for enabling on-demand network access to a sharedpool of configurable computing resources. For example, cloud computingcan be employed in the marketplace to offer ubiquitous and convenienton-demand access to the shared pool of configurable computing resources.The shared pool of configurable computing resources can be rapidlyprovisioned via virtualization and released with low management effortor service provider interaction, and then scaled accordingly.

A cloud-computing model can be composed of various characteristics suchas, for example, on-demand self-service, broad network access, resourcepooling, rapid elasticity, measured service, and so forth. Acloud-computing model can also expose various service models, such as,for example, Software as a Service (“SaaS”), Platform as a Service(“PaaS”), and Infrastructure as a Service (“IaaS”). A cloud-computingmodel can also be deployed using different deployment models such asprivate cloud, community cloud, public cloud, hybrid cloud, and soforth. In this description and in the claims, a “cloud-computingenvironment” is an environment in which cloud computing is employed.

FIG. 8 illustrates, in block diagram form, an example computing device800 (e.g., the computing device 800, a client device 108, a serviceprovider device 112, and/or the server(s) 106) that may be configured toperform one or more of the processes described above. One willappreciate that the unsupervised matching system 102 can compriseimplementations of the computing device 800. As shown by FIG. 8, thecomputing device can comprise a processor 802, memory 804, a storagedevice 806, an I/O interface 808, and a communication interface 810.Furthermore, the computing device 800 can include an input device suchas a touchscreen, mouse, keyboard, etc. In certain embodiments, thecomputing device 800 can include fewer or more components than thoseshown in FIG. 8. Components of computing device 800 shown in FIG. 8 willnow be described in additional detail.

In particular embodiments, processor(s) 802 includes hardware forexecuting instructions, such as those making up a computer program. Asan example, and not by way of limitation, to execute instructions,processor(s) 802 may retrieve (or fetch) the instructions from aninternal register, an internal cache, memory 804, or a storage device806 and decode and execute them.

The computing device 800 includes memory 804, which is coupled to theprocessor(s) 802. The memory 804 may be used for storing data, metadata,and programs for execution by the processor(s). The memory 804 mayinclude one or more of volatile and non-volatile memories, such asRandom-Access Memory (“RAM”), Read Only Memory (“ROM”), a solid-statedisk (“SSD”), Flash, Phase Change Memory (“PCM”), or other types of datastorage. The memory 804 may be internal or distributed memory.

The computing device 800 includes a storage device 806 includes storagefor storing data or instructions. As an example, and not by way oflimitation, storage device 806 can comprise a non-transitory storagemedium described above. The storage device 806 may include a hard diskdrive (HDD), flash memory, a Universal Serial Bus (USB) drive or acombination of these or other storage devices.

The computing device 800 also includes one or more input or output(“I/O”) devices/interfaces 808, which are provided to allow a user toprovide input to (such as user strokes), receive output from, andotherwise transfer data to and from the computing device 800. These I/Odevices/interfaces 808 may include a mouse, keypad or a keyboard, atouch screen, camera, optical scanner, network interface, modem, otherknown I/O devices or a combination of such I/O devices/interfaces 808.The touch screen may be activated with a writing device or a finger.

The I/O devices/interfaces 808 may include one or more devices forpresenting output to a user, including, but not limited to, a graphicsengine, a display (e.g., a display screen), one or more output drivers(e.g., display drivers), one or more audio speakers, and one or moreaudio drivers. In certain embodiments, devices/interfaces 808 isconfigured to provide graphical data to a display for presentation to auser. The graphical data may be representative of one or more graphicaluser interfaces and/or any other graphical content as may serve aparticular implementation.

The computing device 800 can further include a communication interface810. The communication interface 810 can include hardware, software, orboth. The communication interface 810 can provide one or more interfacesfor communication (such as, for example, packet-based communication)between the computing device and one or more other computing devices 800or one or more networks. As an example, and not by way of limitation,communication interface 810 may include a network interface controller(NIC) or network adapter for communicating with an Ethernet or otherwire-based network or a wireless NIC (WNIC) or wireless adapter forcommunicating with a wireless network, such as a WI-FI. The computingdevice 800 can further include a bus 812. The bus 812 can comprisehardware, software, or both that couples components of computing device800 to each other.

In the foregoing specification, the invention has been described withreference to specific example embodiments thereof. Various embodimentsand aspects of the invention(s) are described with reference to detailsdiscussed herein, and the accompanying drawings illustrate the variousembodiments. The description above and drawings are illustrative of theinvention and are not to be construed as limiting the invention.Numerous specific details are described to provide a thoroughunderstanding of various embodiments of the present invention.

The present invention may be embodied in other specific forms withoutdeparting from its spirit or essential characteristics. The describedembodiments are to be considered in all respects only as illustrativeand not restrictive. For example, the methods described herein may beperformed with less or more steps/acts or the steps/acts may beperformed in differing orders. Additionally, the steps/acts describedherein may be repeated or performed in parallel with one another or inparallel with different instances of the same or similar steps/acts. Thescope of the invention is, therefore, indicated by the appended claimsrather than by the foregoing description. All changes that come withinthe meaning and range of equivalency of the claims are to be embracedwithin their scope.

FIG. 9 illustrates an example network environment 900 of a communicationmanagement system (e.g., the communication management system 104).Network environment 900 includes a contacting user device 902 (e.g., aclient device 108), a communication management system 104, a third partysystem 906 (e.g., the third-party server(s) 120), and an agent device908 (e.g., a service provider device 112), connected to each other by anetwork 904 (e.g., the network 122 as shown in FIG. 1). Although FIG. 9illustrates a particular arrangement of the contacting user device 902,the communication management system 104, the agent device 908, thethird-party system 906, and network 904, this disclosure contemplatesany suitable arrangement of the contacting user device 902, thecommunication management system 104, the third-party system 906, theagent device 908, and the network 904. As an example, and not by way oflimitation, two or more of the contacting user device 902, thecommunication management system 104, the agent device 908, and thethird-party system 906 may be connected to each other directly,bypassing network 904. As another example, two or more of the contactinguser device 902, the communication management system 104, the agentdevice 908, and the third-party system 906 may be physically orlogically co-located with each other in whole or in part. Moreover,although FIG. 9 illustrates a particular number of contacting userdevices 902, communication management system 104, agent devices 908,third-party systems 906, and networks 904, this disclosure contemplatesany suitable number of contacting user devices 902, communicationmanagement system 104, agent devices 908, third-party systems 906, andnetworks 904. As an example, and not by way of limitation, networkenvironment 900 may include multiple contacting user devices 902,communication management system 104, agent devices 908, third-partysystems 906, and networks 904.

This disclosure contemplates any suitable network 904. As an example,and not by way of limitation, one or more portions of the network 904may include an ad hoc network, an intranet, an extranet, a virtualprivate network (VPN), a local area network (LAN), a wireless LAN(WLAN), a wide area network (WAN), a wireless WAN (WWAN), a metropolitanarea network (MAN), a portion of the Internet, a portion of the PublicSwitched Telephone Network (PSTN), a cellular telephone network, or acombination of two or more of these. The network 904 may include one ormore networks 904.

Links may connect the contacting user device 902, the communicationmanagement system 104, the agent device 908, and the third-party system906 to communication network 904 or to each other. This disclosurecontemplates any suitable links. In particular embodiments, one or morelinks include one or more wireline (such as for example DigitalSubscriber Line (DSL) or Data Over Cable Service Interface Specification(DOCSIS)), wireless (such as for example Wi-Fi or WorldwideInteroperability for Microwave Access (WiMAX)), or optical (such as forexample Synchronous Optical Network (SONET) or Synchronous DigitalHierarchy (SDH)) links. In particular embodiments, one or more linkseach include an ad hoc network, an intranet, an extranet, a VPN, a LAN,a WLAN, a WAN, a WWAN, a MAN, a portion of the Internet, a portion ofthe PSTN, a cellular technology-based network, a satellitecommunications technology-based network, another link, or a combinationof two or more such links. Links need not necessarily be the samethroughout network environment 900. One or more first links may differin one or more respects from one or more second links.

In particular embodiments, the contacting user device 902 and/or agentdevice 908 may be an electronic device including hardware, software, orembedded logic components or a combination of two or more suchcomponents capable of carrying out the appropriate functionalitiesimplemented or supported by the contacting user device 902 and/or agentdevice 908. As an example, and not by way of limitation, a contactinguser device 902 or agent device 908 may include any of the computingdevices discussed above in relation to FIG. 8. A contacting user device902 may enable a network user (e.g., a contacting user) at thecontacting user device 902 to access the network 904. A contacting userdevice 902 may enable its user to communicate with other users at othercontacting user devices 902 and/or agent devices 908. Likewise, an agentdevice 908 may enable its user to communicate with contacting userdevices 902, third-party systems 906, and/or the communicationmanagement system 104.

In particular embodiments, the agent device 908 is part of (e.g., housedwithin, either entirely or in part) the third-party system 906. Forexample, the third-party system 906 may include a service system whichmaintains one or more agent users, each associated with a separate agentdevice 908. Accordingly, the communication management system 104provides various functionality, user interfaces, electroniccommunications, and other information to the agent device 908 via thethird-party system 906. In other embodiments, the agent device 908 isnot associated with or part of a third-party system 906. Rather, theagent device 908 refers to a client device associated directly with thecommunication management system 104 or a different third-party and thatcommunicates with the communication management system 104 withoutinteraction via the third-party system 906.

In particular embodiments, the contacting user device 902 and/or theagent device 908 may include a web browser, such as MICROSOFT INTERNETEXPLORER, GOOGLE CHROME or MOZILLA FIREFOX, and may have one or moreadd-ons, plug-ins, or other extensions, such as TOOLBAR or YAHOOTOOLBAR. A user at contacting user device 902 and/or agent device 908may enter a Uniform Resource Locator (URL) or other address directingthe web browser to a particular server (such as server, or a serverassociated with a third-party system 906), and the web browser maygenerate a Hyper Text Transfer Protocol (HTTP) request and communicatethe HTTP request to server. The server may accept the HTTP request andcommunicate to the contacting user device 902 (or the agent device 908)one or more Hyper Text Markup Language (HTML) files responsive to theHTTP request. The contacting user device 902 and/or agent device 908 mayrender a webpage based on the HTML files from the server forpresentation to the user. This disclosure contemplates any suitablewebpage files. As an example, and not by way of limitation, webpages mayrender from HTML files, Extensible Hyper Text Markup Language (XHTML)files, or Extensible Markup Language (XML) files, according toparticular needs. Such pages may also execute scripts such as, forexample and without limitation, those written in JAVASCRIPT, JAVA,MICROSOFT SILVERLIGHT, combinations of markup language and scripts suchas AJAX (Asynchronous JAVASCRIPT and XML), and the like. Herein,reference to a webpage encompasses one or more corresponding webpagefiles (which a browser may use to render the webpage) and vice versa,where appropriate.

In particular embodiments, the communication management system 104 maybe a network-addressable computing system that can host an onlinecommunication network. The communication management system 104 maygenerate, store, receive, and send messaging data, such as, for example,text data, audio data, and video data. The communication managementsystem 104 can further generate, store, receive, and send other datasuch as user-profile data, location data, or other suitable data relatedto the online communication network. The communication management system104 may be accessed by the other components of network environment 900either directly or via network 904. In particular embodiments, thecommunication management system 104 may include one or more servers.Each server may be a unitary server or a distributed server spanningmultiple computers or multiple datacenters. Servers may be of varioustypes, such as, for example and without limitation, a web server, acommunication server, a news server, a mail server, a message server, anadvertising server, a file server, an application server, an exchangeserver, a database server, a proxy server, or another server suitablefor performing functions or processes described herein, or anycombination thereof. In particular embodiments, each server may includehardware, software, or embedded logic components or a combination of twoor more such components for carrying out the appropriate functionalitiesimplemented or supported by server.

In particular embodiments, the communication management system 104 mayinclude one or more data stores. Data stores may be used to storevarious types of information. In particular embodiments, the informationstored in data stores may be organized according to specific datastructures. In particular embodiments, each data store may be arelational, columnar, correlation, or other suitable database. Althoughthis disclosure describes or illustrates particular types of databases,this disclosure contemplates any suitable types of databases. Particularembodiments may provide interfaces that enable a contacting user device902, a communication management system 104, an agent device 908, or athird-party system 906 to manage, retrieve, modify, add, or delete, theinformation stored in data store.

In particular embodiments, the communication management system 104 maystore one or more correspondences in one or more data stores. Inparticular embodiments, a correspondence may include electronicmessages—which may include digital audio, digital video, and/or digitaltext (e.g., as received in an electronic message or as transcribed fromdigital audio). The communication management system 104 may provideusers (e.g., contacting users) of the online communication network theability to communicate and interact with other users (e.g., agentusers). In particular embodiments, agent users may join the onlinecommunication network via the communication management system 104, andthe communication management system 104 may maintain connects for theagent users, including current and historical information relating tocontacting users with whom the agent users interact.

In particular embodiments, the communication management system 104 mayprovide agent users with the ability to take actions on various types ofitems or objects, supported by communication management system 104. Asan example, and not by way of limitation, the items and objects mayinclude groups or individual contacting users communicating with thecommunication management system 104, computer-based applications that auser may use, transactions that allow agent users to buy, return, orsell items or services, indications that allow agent users to reportevents, or other interactions with various operations that an agent usermay perform to assist a contacting user.

In particular embodiments, the communication management system 104 maybe capable of linking a variety of entities. As an example, and not byway of limitation, the communication management system 104 may enableusers such as contacting users and agent users to interact with eachother as well as receive content from third-party systems 906 or otherentities, or to allow users to interact with these entities through anapplication programming interfaces (API) or other communicationchannels.

In particular embodiments, a third-party system 906 may include one ormore types of servers, one or more data stores, one or more interfaces,including but not limited to APIs, one or more web services, one or morecontent sources, one or more networks, or any other suitable components,e.g., that servers may communicate with. A third-party system 906 may beoperated by a different entity from an entity operating thecommunication management system 104. In particular embodiments, however,the communication management system 104 and third-party systems 906 mayoperate in conjunction with each other to provide communication servicesto users of the communication management system 104 or third-partysystems 906. In this sense, the communication management system 104 mayprovide a platform, or backbone, which other systems, such asthird-party systems 906, may use to provide communication services andfunctionality to users across the Internet or other network.

In particular embodiments, a third-party system 906 may include athird-party service system. A third-party content service system mayinclude one or more sources of products or services that are used bycontacting user. As an example, and not by way of limitation, a servicesystem can include a traffic monitoring system, a social networkingsystem, an emergency reporting system, a home repair system, a weathermonitoring system, an internet service provider system, an airlinesystem, a transportation provider system, a news media system, a travelaccommodation system, an electronic smart appliance distribution ormonitoring system, or a power distribution or monitoring system.

In particular embodiments, the communication management system 104 mayinclude a variety of servers, sub-systems, programs, modules, logs, anddata stores. In particular embodiments, the communication managementsystem 104 may include one or more of the following: a web server,action logger, API-request server, notification controller, action log,inference module, search module, advertisement-targeting module,user-interface module, user-profile store, third-party content store, orlocation store. The communication management system 104 may also includesuitable components such as network interfaces, security mechanisms,load balancers, failover servers, management-and-network-operationsconsoles, other suitable components, or any suitable combinationthereof. In particular embodiments, the communication management system104 may include one or more user-profile stores for storing userprofiles (e.g., for contacting users). A user profile may include, forexample, contacting user information, biographic information,demographic information, behavioral information, social information, orother types of descriptive information, such as work experience,educational history, hobbies or preferences, interests, affinities, orlocation.

The web server may include a mail server or other messagingfunctionality for receiving and routing messages between thecommunication management system 104 and one or more contacting userdevices 902 or agent devices 906. An API-request server may allow athird-party system 906 to access information from the communicationmanagement system 104 by calling one or more APIs. An action logger maybe used to receive communications from a web server about a user'sactions on or off the communication management system 104. Informationmay be pushed to a contacting user device 902 and/or agent device 908 asnotifications, or information may be pulled from contacting user device902 responsive to a request received from contacting user device 902 oragent device 908.

We claim:
 1. A method comprising: identifying match prediction trainingdata, wherein the match prediction training data comprises a pluralityof instances of raw communication match data and a plurality of serviceprovider match markers, and further wherein each instance of rawcommunication match data of the plurality of instances of rawcommunication match data corresponds to a service provider match markerof the plurality of service provider match markers; and training aservice provider neural network using the match prediction training databy, for each instance of raw communication data: passing the instance ofraw communication data to the service provider match neural network;receiving a prediction from the service provider match neural networkfor the instance of raw communication data; comparing the prediction tothe service provider match marker corresponding to the instance of rawcommunication match data; and modifying one or more parameters of one ormore layers of the service provider match neural network based on thecomparing.
 2. The method of claim 1, wherein passing the instance of rawcommunication data to the service provider match neural networkcomprises: generating an input vector from the instance of rawcommunication data; and passing the generated input vector to theservice provider match neural network.
 3. The method of claim 1, whereineach instance of raw communication data comprises recorded audio, andeach corresponding service provider match marker comprises one or moreof a service provider identifier or a plurality of attributes of apredicted service provider.
 4. The method of claim 1, wherein theprediction for the instance of raw communication data comprises a firstplurality of attributes and the service provider match markercorresponding to the instance of raw communication data comprises asecond plurality of attributes, and comparing the prediction to theservice provider match marker corresponding to the instance of rawcommunication match data comprises: determining that more than athreshold number of attributes from the first plurality of attributesmatch attributes from the second plurality of attributes; and inresponse to the determination, determining that the prediction for theinstance of raw communication data matches the service provider matchmarker corresponding to the instance of raw communication data.
 5. Themethod of claim 1, further comprising: receiving a communication from aclient device associated with a contacting user; extracting rawcommunication data associated with the contacting user from the receivedcommunication; providing the extracted raw communication data to theservice provider match neural network; receiving a first serviceprovider match prediction from the service provider match neural networkbased on the provided extracted raw communication data; and providingthe first service provider match prediction to a customer requestsystem.
 6. The method of claim 5, further comprising: receiving, fromthe customer request system, an indication that the first serviceprovider match prediction cannot be accommodated; in response toreceiving the indication, modifying one or more parameters of one ormore layers of the service provider match neural network; providing theextracted raw communication data to the service provider match neuralnetwork; and receiving a second service provider match prediction fromthe service provider match neural network based on the extracted rawcommunication data.
 7. The method of claim 5, wherein providing theextracted raw communication data to the service provider match neuralnetwork comprises: generating an input vector based on the rawcommunication data; and providing the generated input vector to theservice provider match neural network.
 8. A system comprising: at leastone processor; and at least one non-transitory computer-readable storagemedium storing instructions thereon that, when executed by the at leastone processor, cause the system to: identify match prediction trainingdata, wherein the match prediction training data comprises a pluralityof instances of raw communication match data and a plurality of serviceprovider match markers, and further wherein each instance of rawcommunication match data of the plurality of instances of rawcommunication match data corresponds to a service provider match markerof the plurality of service provider match markers; and train a serviceprovider neural network using the match prediction training data by, foreach instance of raw communication data: passing the instance of rawcommunication data to the service provider match neural network;receiving a prediction from the service provider match neural networkfor the instance of raw communication data; comparing the prediction tothe service provider match marker corresponding to the instance of rawcommunication match data; and modifying one or more parameters of one ormore layers of the service provider match neural network based on thecomparing.
 9. The system of claim 8, wherein passing the instance of rawcommunication data to the service provider match neural networkcomprises: generating an input vector from the instance of rawcommunication data; and passing the generated input vector to theservice provider match neural network.
 10. The system of claim 8,wherein each instance of raw communication data comprises recordedaudio, and each corresponding service provider match marker comprisesone or more of a service provider identifier or a plurality ofattributes of a predicted service provider.
 11. The system of claim 8,wherein the prediction for the instance of raw communication datacomprises a first plurality of attributes and the service provider matchmarker corresponding to the instance of raw communication data comprisesa second plurality of attributes, and comparing the prediction to theservice provider match marker corresponding to the instance of rawcommunication match data comprises: determining that more than athreshold number of attributes from the first plurality of attributesmatch attributes from the second plurality of attributes; and inresponse to the determination, determining that the prediction for theinstance of raw communication data matches the service provider matchmarker corresponding to the instance of raw communication data.
 12. Thesystem of claim 8, further storing instructions thereon that, whenexecuted by the at least one processor, cause the system to: receive acommunication from a client device associated with a contacting user;extract raw communication data associated with the contacting user fromthe received communication; provide the extracted raw communication datato the service provider match neural network; receive a first serviceprovider match prediction from the service provider match neural networkbased on the provided extracted raw communication data; and provide thefirst service provider match prediction to a customer request system.13. The system of claim 12, further storing instructions thereon that,when executed by the at least one processor, cause the system to:receive, from the customer request system, an indication that the firstservice provider match prediction cannot be accommodated; in response toreceiving the indication, modify one or more parameters of one or morelayers of the service provider match neural network; provide theextracted raw communication data to the service provider match neuralnetwork; and receive a second service provider match prediction from theservice provider match neural network based on the extracted rawcommunication data.
 14. The system of claim 13, wherein providing theextracted raw communication data to the service provider match neuralnetwork comprises: generating an input vector based on the rawcommunication data; and providing the generated input vector to theservice provider match neural network.
 15. A non-transitorycomputer-readable medium storing instructions thereon that, whenexecuted by at least one processor, cause a computer system to: identifymatch prediction training data, wherein the match prediction trainingdata comprises a plurality of instances of raw communication match dataand a plurality of service provider match markers, and further whereineach instance of raw communication match data of the plurality ofinstances of raw communication match data corresponds to a serviceprovider match marker of the plurality of service provider matchmarkers; and train a service provider neural network using the matchprediction training data by, for each instance of raw communicationdata: passing the instance of raw communication data to the serviceprovider match neural network; receiving a prediction from the serviceprovider match neural network for the instance of raw communicationdata; comparing the prediction to the service provider match markercorresponding to the instance of raw communication match data; andmodifying one or more parameters of one or more layers of the serviceprovider match neural network based on the comparing.
 16. Thecomputer-readable medium of claim 15, wherein passing the instance ofraw communication data to the service provider match neural networkcomprises: generating an input vector from the instance of rawcommunication data; and passing the generated input vector to theservice provider match neural network.
 17. The computer-readable mediumof claim 15, wherein each instance of raw communication data comprisesrecorded audio, and each corresponding service provider match markercomprises one or more of a service provider identifier or a plurality ofattributes of a predicted service provider.
 18. The computer-readablemedium of claim 15, wherein the prediction for the instance of rawcommunication data comprises a first plurality of attributes and theservice provider match marker corresponding to the instance of rawcommunication data comprises a second plurality of attributes, andcomparing the prediction to the service provider match markercorresponding to the instance of raw communication match data comprises:determining that more than a threshold number of attributes from thefirst plurality of attributes match attributes from the second pluralityof attributes; and in response to the determination, determining thatthe prediction for the instance of raw communication data matches theservice provider match marker corresponding to the instance of rawcommunication data.
 19. The computer-readable medium of claim 15,further storing instructions thereon that, when executed by the at leastone processor, cause the computer system to: receive a communicationfrom a client device associated with a contacting user; extract rawcommunication data associated with the contacting user from the receivedcommunication; provide the extracted raw communication data to theservice provider match neural network; receive a first service providermatch prediction from the service provider match neural network based onthe provided extracted raw communication data; and provide the firstservice provider match prediction to a customer request system.
 20. Thecomputer-readable medium of claim 19, further storing instructionsthereon that, when executed by the at least one processor, cause thecomputer system to: receive, from the customer request system, anindication that the first service provider match prediction cannot beaccommodated; in response to receiving the indication, modify one ormore parameters of one or more layers of the service provider matchneural network; provide the extracted raw communication data to theservice provider match neural network; and receive a second serviceprovider match prediction from the service provider match neural networkbased on the extracted raw communication data.